(Invited) Binary Stochastic Neurons and Compound Synapses from Nano-Magnets

Abstract

In this presentation I will introduce the nCORE center CAPSL’s activities towards a probabilistic spin logic for Boolean and Non-Boolean computing. While magnetic materials in storage devices are part of our electronic world since many decades and are explored more recently by the semiconductor industry for memory applications such as Spin-transfer torque magnetic random-access memory (STT-RAM), spin based devices for logic applications are still in their infancies. Mimicking a CMOS type logic with magnets requires a “Write” and a “Read” unit as well as proper input/output isolation. Based on this, a device concept in which charge-to-spin conversion occurs through the Giant Spin Hall Effect (GSHE) [1], dipolar coupling mediates the information transfer from a magnet in the Write unit to another one in the Read unit, and a magnetic tunnel junction (MTJ) is used as the Read unit was proposed in 2012 under the name charge spin logic (CSL) [2]. Next, I will explain in how far the use of low-barrier magnets cannot only help to reduce power consumption of CSL devices but introduces a conceptually new way of computing through the introduction of “tunable randomness”. This idea, that is also known as probabilistic spin logic (PSL), was introduced in 2017 [3] and uses a counterintuitive aspect of magnets, namely their capability of creating a truly random magnetic signal if low-barrier circular nano-magnets are used. In particular, I will connect PSL with activities in the realm of quantum computing as well as neural networks.[1] L. Liu, C.-F. Pai, Y. Li, H. W. Tseng, D. C. Ralph, and R. a. Buhrman, Science 336, 555 (2012).[2] S. Datta, S. Salahuddin, and B. Behin-Aein, Applied Physics Letters 101, 252411 (2012).[3] K. Y. Camsari, R. Faria, B. Sutton and S. Datta. Physical Review X 7, 031014 (2017).